Image Analysis and Computer Vision

Semester:

7th

Course Type:

Elective Specialization courses (ΠΜ-E)

Track:

-

Code:

ΕΠ23

ECTS:

6

TEACHING HOURS per week

Theory:

4

Seminar:

-

Laboratory:

-

Specializations

Foundations of Computer Science (S1):

-

Data and Knowledge Management (S2):

-

Software (S3):

-

Hardware and Architecture (S4):

-

Communications and Networking (S5):

-

Signal and Information Processing (S6):

-

Related Courses

Detailed Description

DIT_PPS_ΕΠ23-Image Analysis and Computer Vision.pdf

Course Content

Mathematical foundations of computer vision.
Projective geometry, image formation, and stereoscopy.
Basic concepts of digital images and neighborhood (local) operations.
Feature extraction and edge detection.
Corner detection and multi-scale pyramids.
Scale space, blob detectors, and local feature descriptors.
Advanced local features and vector representations of images.
Basic concepts of machine learning.
Introduction to traditional image recognition.
Optical flow.
Introduction to neural networks and the PyTorch library.
Convolutional Neural Networks.
The attention mechanism and the Transformer architecture.
Object detection.
Semantic image segmentation.
Self-supervised representation learning and vision-language representations.
Image synthesis with generative modeling.

LITERATURE AND STUDY MATERIALS - READING LIST

Computer Vision, 2nd Edition, R. Szeliski, Ed. Fountas, 2022 (Greek).
Machine Learning, K. Diamantaras and D. Botsis, Ed. Kleidarithmos, 2019.
Image Processing and Analysis, G. Tziritas and N. Komodakis, Kallipos, 2023.

e-Class link

Image Analysis and Computer Vision - ECLASS